Roboticists build new nimble-fingered robot

Roboticists at the University of California, Berkeley, have built a new robot that can pick up and move unfamiliar, real-world objects with a 99 percent success rate.

The dexterous robot, called DexNet 2.0, gained its highly accurate dexterity through a process called deep learning.

Irregularly shaped items such as shoes, spray bottles, open boxes, rubber duckies are easy for people to grab and pick up, but robots struggle with knowing where to apply a grip.

UC Berkeley professor Ken Goldberg, postdoctoral researcher Jeff Mahler and the Laboratory for Automation Science and Engineering (AUTOLAB) created DexNet 2.0 with the support of a vast database of three-dimensional shapes, 6.7 million data points in total, that a neural network uses to learn grasps, which in turn will pick up and move objects with irregular shapes.

The neural network was then connected to a 3D sensor and a robotic arm. When an object is placed in front of DexNet 2.0, it quickly studies the shape and selects a grasp that will successfully pick up and move the object 99 percent of the time.

Featured as the cover story of the latest issues of MIT Technology Review, DexNet 2.0 is also three times faster than its previous version.

With such a high success rate, claimed UC Berkeley’s Industrial Engineering and Operations Research Department in a news release. “it is likely that this work will soon be applied in industry, possibly revolutionizing manufacturing and the supply chain.”

AUTOLAB at the public university in Northern California is a world-renowned center for research in robotics and automation sciences, with more than 30 postdoctoral researchers, doctoral and undergraduate students pursuing projects in Cloud Robotics, Deep Reinforcement Learning, Learning from Demonstrations, Computer Assisted Surgery, Automated Manufacturing and New Media Artforms.